Brendel, Wieland

71 publications

ICLR 2025 Cross-Entropy Is All You Need to Invert the Data Generating Process Patrik Reizinger, Alice Bizeul, Attila Juhos, Julia E Vogt, Randall Balestriero, Wieland Brendel, David Klindt

ICLR 2025 Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning Patrik Reizinger, Siyuan Guo, Ferenc Huszár, Bernhard Schölkopf, Wieland Brendel

ICLRW 2025 Implicit Bayesian Inference Is an Insufficient Explanation of Language Model Behaviour in Compositional Tasks Szilvia Ujváry, Anna Mészáros, Wieland Brendel, Patrik Reizinger, Ferenc Huszár

ICLR 2025 In Search of Forgotten Domain Generalization Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel

ICLRW 2025 In Search of Forgotten Domain Generalization Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel

AISTATS 2025 InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel

ICLR 2025 Interaction Asymmetry: A General Principle for Learning Composable Abstractions Jack Brady, Julius von Kügelgen, Sebastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel

ICML 2025 LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann

ICLRW 2025 LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models Fanfei Li, Thomas Klein, Wieland Brendel, Robert Geirhos, Roland S. Zimmermann

ICML 2025 LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Prasanna Mayilvahanan, Thaddäus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel

ICLRW 2025 LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws Prasanna Mayilvahanan, Thaddäus Wiedemer, Sayak Mallick, Matthias Bethge, Wieland Brendel

TMLR 2025 Occam’s Razor for SSL: Memory-Efficient Parametric Instance Discrimination Eric Gan, Patrik Reizinger, Alice Bizeul, Attila Juhos, Mark Ibrahim, Randall Balestriero, David Klindt, Wieland Brendel, Baharan Mirzasoleiman

ICML 2025 Position: An Empirically Grounded Identifiability Theory Will Accelerate Self Supervised Learning Research Patrik Reizinger, Randall Balestriero, David Klindt, Wieland Brendel

NeurIPS 2025 Quantifying Uncertainty in Error Consistency: Towards Reliable Behavioral Comparison of Classifiers Thomas Klein, Sascha Meyen, Wieland Brendel, Felix A. Wichmann, Kristof Meding

ICCV 2025 VGGSounder: Audio-Visual Evaluations for Foundation Models Daniil Zverev, Thaddäus Wiedemer, Ameya Prabhu, Matthias Bethge, Wieland Brendel, A. Sophia Koepke

CLeaR 2024 An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis Goutham Rajendran, Patrik Reizinger, Wieland Brendel, Pradeep Kumar Ravikumar

NeurIPSW 2024 DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features Attila Juhos, Alice Bizeul, Patrik Reizinger, Randall Balestriero, David Klindt, Mark Ibrahim, Julia E Vogt, Wieland Brendel

NeurIPSW 2024 DIETing: Self-Supervised Learning with Instance Discrimination Learns Identifiable Features Attila Juhos, Alice Bizeul, Patrik Reizinger, David Klindt, Randall Balestriero, Mark Ibrahim, Julia E Vogt, Wieland Brendel

ICLR 2024 Does CLIP’s Generalization Performance Mainly Stem from High Train-Test Similarity? Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel

ICML 2024 Don’t Trust Your Eyes: On the (un)reliability of Feature Visualizations Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, Been Kim

ICLR 2024 Effective Pruning of Web-Scale Datasets Based on Complexity of Concept Clusters Amro Kamal Mohamed Abbas, Evgenia Rusak, Kushal Tirumala, Wieland Brendel, Kamalika Chaudhuri, Ari S. Morcos

ICMLW 2024 In Search of Forgotten Domain Generalization Prasanna Mayilvahanan, Roland S. Zimmermann, Thaddäus Wiedemer, Evgenia Rusak, Attila Juhos, Matthias Bethge, Wieland Brendel

ICMLW 2024 InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel

ICMLW 2024 InfoNCE: Identifying the Gap Between Theory and Practice Evgenia Rusak, Patrik Reizinger, Attila Juhos, Oliver Bringmann, Roland S. Zimmermann, Wieland Brendel

NeurIPSW 2024 Interaction Asymmetry: A General Principle for Learning Composable Abstractions Jack Brady, Julius von Kügelgen, Sebastien Lachapelle, Simon Buchholz, Wieland Brendel

ICLRW 2024 Measuring Mechanistic Interpretability at Scale Without Humans Roland S. Zimmermann, David A. Klindt, Wieland Brendel

NeurIPS 2024 Measuring Per-Unit Interpretability at Scale Without Humans Roland S. Zimmermann, David Klindt, Wieland Brendel

ICML 2024 Position: Understanding LLMs Requires More than Statistical Generalization Patrik Reizinger, Szilvia Ujváry, Anna Mészáros, Anna Kerekes, Wieland Brendel, Ferenc Huszár

NeurIPSW 2024 Pretraining Frequency Predicts Compositional Generalization of CLIP on Real-World Tasks Thaddäus Wiedemer, Yash Sharma, Ameya Prabhu, Matthias Bethge, Wieland Brendel

ICLR 2024 Provable Compositional Generalization for Object-Centric Learning Thaddäus Wiedemer, Jack Brady, Alexander Panfilov, Attila Juhos, Matthias Bethge, Wieland Brendel

ICLRW 2024 Removing High Frequency Information Improves DNN Behavioral Alignment Max Wolff, Evgenia Rusak, Wieland Brendel

NeurIPS 2024 Rule Extrapolation in Language Modeling: A Study of Compositional Generalization on OOD Prompts Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszár

NeurIPSW 2024 Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts Anna Mészáros, Szilvia Ujváry, Wieland Brendel, Patrik Reizinger, Ferenc Huszár

NeurIPSW 2024 Towards Object-Centric Learning with General Purpose Architectures Jack Brady, Julius von Kügelgen, Sebastien Lachapelle, Simon Buchholz, Thomas Kipf, Wieland Brendel

NeurIPS 2023 Compositional Generalization from First Principles Thaddäus Wiedemer, Prasanna Mayilvahanan, Matthias Bethge, Wieland Brendel

ICMLW 2023 Desiderata for Representation Learning from Identifiability, Disentanglement, and Group-Structuredness Hamza Keurti, Patrik Reizinger, Bernhard Schölkopf, Wieland Brendel

NeurIPSW 2023 Does CLIP’s Generalization Performance Mainly Stem from High Train-Test Similarity? Prasanna Mayilvahanan, Thaddäus Wiedemer, Evgenia Rusak, Matthias Bethge, Wieland Brendel

ICMLW 2023 Don't Trust Your Eyes: On the (un)reliability of Feature Visualizations Robert Geirhos, Roland S. Zimmermann, Blair Bilodeau, Wieland Brendel, Been Kim

TMLR 2023 Jacobian-Based Causal Discovery with Nonlinear ICA Patrik Reizinger, Yash Sharma, Matthias Bethge, Bernhard Schölkopf, Ferenc Huszár, Wieland Brendel

ICML 2023 Provably Learning Object-Centric Representations Jack Brady, Roland S. Zimmermann, Yash Sharma, Bernhard Schölkopf, Julius Von Kügelgen, Wieland Brendel

NeurIPS 2023 Scale Alone Does Not Improve Mechanistic Interpretability in Vision Models Roland S. Zimmermann, Thomas Klein, Wieland Brendel

NeurIPSW 2023 Scale Alone Does Not Improve Mechanistic Interpretability in Vision Models Roland Zimmermann, Thomas Klein, Wieland Brendel

ICLRW 2023 The Independent Compositional Subspace Hypothesis for the Structure of CLIP's Last Layer Max Wolff, Wieland Brendel, Stuart Wolff

NeurIPS 2022 Embrace the Gap: VAEs Perform Independent Mechanism Analysis Patrik Reizinger, Luigi Gresele, Jack Brady, Julius von Kügelgen, Dominik Zietlow, Bernhard Schölkopf, Georg Martius, Wieland Brendel, Michel Besserve

TMLR 2022 If Your Data Distribution Shifts, Use Self-Learning Evgenia Rusak, Steffen Schneider, George Pachitariu, Luisa Eck, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge

ICMLW 2022 ImageNet-D: A New Challenging Robustness Dataset Inspired by Domain Adaptation Evgenia Rusak, Steffen Schneider, Peter Vincent Gehler, Oliver Bringmann, Wieland Brendel, Matthias Bethge

NeurIPS 2022 Increasing Confidence in Adversarial Robustness Evaluations Roland S. Zimmermann, Wieland Brendel, Florian Tramer, Nicholas Carlini

ICLR 2022 Visual Representation Learning Does Not Generalize Strongly Within the Same Domain Lukas Schott, Julius Von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel

JMLR 2021 Benchmarking Unsupervised Object Representations for Video Sequences Marissa A. Weis, Kashyap Chitta, Yash Sharma, Wieland Brendel, Matthias Bethge, Andreas Geiger, Alexander S. Ecker

ICML 2021 Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, Wieland Brendel

ICLR 2021 Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel

NeurIPS 2021 Fast Minimum-Norm Adversarial Attacks Through Adaptive Norm Constraints Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio

ICMLW 2021 Fast Minimum-Norm Adversarial Attacks Through Adaptive Norm Constraints Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio

NeurIPS 2021 How Well Do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann, Judy Borowski, Robert Geirhos, Matthias Bethge, Thomas Wallis, Wieland Brendel

NeurIPS 2021 Partial Success in Closing the Gap Between Human and Machine Vision Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Tizian Thieringer, Matthias Bethge, Felix A. Wichmann, Wieland Brendel

NeurIPS 2021 Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, Francesco Locatello

ICLR 2021 Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding David A. Klindt, Lukas Schott, Yash Sharma, Ivan Ustyuzhaninov, Wieland Brendel, Matthias Bethge, Dylan Paiton

ECCV 2020 A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions Evgenia Rusak, Lukas Schott, Roland S. Zimmermann, Julian Bitterwolf, Oliver Bringmann, Matthias Bethge, Wieland Brendel

NeurIPS 2020 Improving Robustness Against Common Corruptions by Covariate Shift Adaptation Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, Matthias Bethge

NeurIPSW 2020 Natural Images Are More Informative for Interpreting CNN Activations than State-of-the-Art Synthetic Feature Visualizations Judy Borowski, Roland Simon Zimmermann, Judith Schepers, Robert Geirhos, Thomas S. A. Wallis, Matthias Bethge, Wieland Brendel

NeurIPS 2020 On Adaptive Attacks to Adversarial Example Defenses Florian Tramer, Nicholas Carlini, Wieland Brendel, Aleksander Madry

NeurIPSW 2020 On the Surprising Similarities Between Supervised and Self-Supervised Models Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A. Wichmann, Wieland Brendel

NeurIPS 2019 Accurate, Reliable and Fast Robustness Evaluation Wieland Brendel, Jonas Rauber, Matthias Kümmerer, Ivan Ustyuzhaninov, Matthias Bethge

ICLR 2019 Approximating CNNs with Bag-of-Local-Features Models Works Surprisingly Well on ImageNet Wieland Brendel, Matthias Bethge

ICLR 2019 ImageNet-Trained CNNs Are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, Wieland Brendel

NeurIPS 2019 Learning from Brains How to Regularize Machines Zhe Li, Wieland Brendel, Edgar Walker, Erick Cobos, Taliah Muhammad, Jacob Reimer, Matthias Bethge, Fabian Sinz, Zachary Pitkow, Andreas Tolias

ICLR 2019 Towards the First Adversarially Robust Neural Network Model on MNIST Lukas Schott, Jonas Rauber, Matthias Bethge, Wieland Brendel

ICLR 2018 Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models Wieland Brendel, Jonas Rauber, Matthias Bethge

ICLR 2017 What Does It Take to Generate Natural Textures? Ivan Ustyuzhaninov, Wieland Brendel, Leon A. Gatys, Matthias Bethge

NeurIPS 2014 Unsupervised Learning of an Efficient Short-Term Memory Network Pietro Vertechi, Wieland Brendel, Christian K. Machens

NeurIPS 2011 Demixed Principal Component Analysis Wieland Brendel, Ranulfo Romo, Christian K. Machens